Model Based Torque Converter Clutch Slip Control
Bibliographic record
Abstract
To realize better fuel economy benefits from transmissions, car makers have started the application of torque converter clutch control in second gear and beyond, resulting in greater demand on the torque converter clutch (TCC) and its control system. This paper focuses on one aspect of the control of the torque converter clutch to improve fuel economy and faster response of the transmission. A TCC is implemented to control the slip between the pump and turbine of the torque converter, thereby increasing its energy transfer efficiency and increasing vehicle fuel economy. However, due to the non-linear nature of the torque converter fluid coupling, the slip feedback control has to be very active to handle different driver inputs and road-load conditions, such as different desired slip levels, changes in engine input torques, etc. This non-linearity requires intense calibration efforts to precisely control the clutch slip in all the scenarios. In this paper, a model based control method is developed to calculate the feedforward portion of the pressure control signal. This feedforward pressure calculation is so accurate that when it is used in conjunction with the slip feedback signal, the control of the torque converter clutch becomes extremely effective in controlling the slip to desired small values and at the same time preventing clutch flares and lock ups. The proposed feedforward algorithm applies the Kotwicki model or an equivalent table lookup to predict torque across the clutch using the desired slip, current estimated engine torque, and turbine speed. This new control strategy provides more accurate control, and reacts faster during the transient conditions, such as engine torque changes and reference slip changes. The strategy has been applied and demonstrated in various torque converters in both RWD and FWD GM transmissions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".